class_docstr: Policy object that implements a DDPG-like actor critic, using a feed forward neural network.
  
  :param sess: (TensorFlow session) The current TensorFlow session
  :param ob_space: (Gym Space) The observation space of the environment
  :param ac_space: (Gym Space) The action space of the environment
  :param n_env: (int) The number of environments to run
  :param n_steps: (int) The number of steps to run for each environment
  :param n_batch: (int) The number of batch to run (n_envs * n_steps)
  :param reuse: (bool) If the policy is reusable or not
  :param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64])
  :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction
  :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp")
  :param layer_norm: (bool) enable layer normalisation
  :param act_fun: (tf.func) the activation function to use in the neural network.
  :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
code: def __init__(
      self,
      sess,
      ob_space,
      ac_space,
      n_env=1,
      n_steps=1,
      n_batch=None,
      reuse=False,
      layers=None,
      cnn_extractor=nature_cnn,
      feature_extraction="cnn",
      layer_norm=False,
      act_fun=tf.nn.relu,
      **kwargs,
  ):
      super(FeedForwardPolicy, self).__init__(
          sess,
          ob_space,
          ac_space,
          n_env,
          n_steps,
          n_batch,
          reuse=reuse,
          scale=(feature_extraction == "cnn"),
      )
  
      self._kwargs_check(feature_extraction, kwargs)
      self.layer_norm = layer_norm
      self.feature_extraction = feature_extraction
      self.cnn_kwargs = kwargs
      self.cnn_extractor = cnn_extractor
      self.reuse = reuse
      if layers is None:
          layers = [64, 64]
      self.layers = layers
  
      assert (
          len(layers) >= 1
      ), "Error: must have at least one hidden layer for the policy."
  
      self.activ_fn = act_fun
  
imports: import tensorflow
  from gym.spaces import Box
  from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
  from stable_baselines.common.tf_layers import mlp
label_desc: Use image node_id 1 to create a new FeedForwardPolicy object from inherited base classes: TD3Policy with example: obj = FeedForwardPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, layers, cnn_extractor, feature_extraction, layer_norm, act_fun)